Self-Driving Car Engineer Nanodegree¶
Project: Finding Lane Lines on the Road¶
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip "raw-lines-example.mp4" (also contained in this repository) to see what the output should look like after using the helper functions below.
Once you have a result that looks roughly like "raw-lines-example.mp4", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video "P1_example.mp4". Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.
In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the rubric points for this project.
Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'. Run the 2 cells below (hit Shift-Enter or the "play" button above) to display the image.
Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the "Kernel" menu above and selecting "Restart & Clear Output".
The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. You are also free to explore and try other techniques that were not presented in the lesson. Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below). Once you have a working pipeline, try it out on the video stream below.
Your output should look something like this (above) after detecting line segments using the helper functions below
Your goal is to connect/average/extrapolate line segments to get output like this
Run the cell below to import some packages. If you get an import error for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.
Import Packages¶
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import datetime
%matplotlib inline
Read in an Image¶
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', image.shape)
plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')
Ideas for Lane Detection Pipeline¶
Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:
cv2.inRange() for color selection
cv2.fillPoly() for regions selection
cv2.line() to draw lines on an image given endpoints
cv2.addWeighted() to coadd / overlay two images
cv2.cvtColor() to grayscale or change color
cv2.imwrite() to output images to file
cv2.bitwise_and() to apply a mask to an image
Check out the OpenCV documentation to learn about these and discover even more awesome functionality!
Helper Functions¶
Below are some helper functions to help get you started. They should look familiar from the lesson!
import math
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
(assuming your grayscaled image is called 'gray')
you should call plt.imshow(graye)"""
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Or use BGR2GRAY if you read an image with cv2.imread()
# return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def canny(img, low_threshold, high_threshold):
"""Applies the Canny transform"""
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
"""
NOTE: this is the function you might want to use as a starting point once you want to
average/extrapolate the line segments you detect to map out the full
extent of the lane (going from the result shown in raw-lines-example.mp4
to that shown in P1_example.mp4).
Think about things like separating line segments by their
slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
line vs. the right line. Then, you can average the position of each of
the lines and extrapolate to the top and bottom of the lane.
This function draws `lines` with `color` and `thickness`.
Lines are drawn on the image inplace (mutates the image).
If you want to make the lines semi-transparent, think about combining
this function with the weighted_img() function below
"""
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
"""
`img` should be the output of a Canny transform.
Returns an image with hough lines drawn.
"""
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
draw_lines(line_img, lines, thickness= 5)
return lines, line_img
# Python 3 has support for cool math symbols.
def weighted_img(img, initial_img, α=0.8, β=1., γ=0.):
"""
`img` is the output of the hough_lines(), An image with lines drawn on it.
Should be a blank image (all black) with lines drawn on it.
`initial_img` should be the image before any processing.
The result image is computed as follows:
initial_img * α + img * β + γ
NOTE: initial_img and img must be the same shape!
"""
return cv2.addWeighted(initial_img, α, img, β, γ)
Test Images¶
Build your pipeline to work on the images in the directory "test_images"
You should make sure your pipeline works well on these images before you try the videos.
import os
os.listdir("test_images/")
Build a Lane Finding Pipeline¶
Build the pipeline and run your solution on all test_images. Make copies into the test_images_output directory, and you can use the images in your writeup report.
Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters.
The Pipeline consists of:¶
- Color Selection
- Gray scaling and Smoothing the Image
- Canny Edge Detection
- Hough Transform
- Extrapolating Lane lines
Note: In the coming sections we'll be applying each step of the pipeline on all of the test images to get a good idea of the performance on a multitude of different examples.
def show_images(images, image_name= '',cmap= None):
SAVE_DIR = 'test_images_output/'
directory = ''
cols = 2
rows = (len(images)+1)//cols
plt.figure(figsize=(10, 11))
for i, image in enumerate(images):
plt.subplot(rows, cols, i+1)
# use gray scale color map if there is only one channel
cmap = 'gray' if len(image.shape)==2 else cmap
plt.imshow(image, cmap=cmap)
plt.xticks([])
plt.yticks([])
plt.tight_layout(pad=0, h_pad=0, w_pad=0)
if os.path.isdir(SAVE_DIR):
directory = SAVE_DIR
image_name = str(datetime.datetime.now()).split('.')[0].replace(' ', '').replace(':','').replace('-', '') if image_name == '' else image_name
plt.savefig(directory + image_name + '.png', bbox_inches = 'tight')
plt.show()
# Read in the test images
test_images = [mpimg.imread("test_images/" + image_name) for image_name in os.listdir("test_images/")]
show_images(test_images, image_name='all_test_images')
Color Selection¶
Here we want to apply multiple strategies to try and get the white and yellow lane lines from our images.
RGB filtering¶
Here we apply masks to the image to only select the White and Yellow object within our image.
def select_white_yellow_colors(image):
# White color mask
lower_white = np.uint8([200, 200, 200])
upper_white = np.uint8([255, 255, 255])
white_mask = cv2.inRange(image, lower_white, upper_white)
# Yellow color mask
lower_yellow = np.uint8([190, 190, 0])
upper_yellow = np.uint8([255, 255, 255])
yellow_mask = cv2.inRange(image, lower_yellow, upper_yellow)
# One mask
mask = cv2.bitwise_or(white_mask, yellow_mask)
masked_image = cv2.bitwise_and(image, image, mask = mask)
return masked_image
show_images(list(map(select_white_yellow_colors, test_images)), image_name='rgb_filtered_test_images')
While this yield decent results we can see that for other images like the ones in the challenge video some of the lines will be a lot darker than others due to shadows from objects like trees and clouds. So we need to explore other color spaces to solve this problem. HSV (Hue, Saturation, Value) and HSL (Hue, Saturation, Lightness) are two good candidates cause we can change their saturation and in the case of HSV we can even play with Lightness
You can checkout how to convert to different color spaces in OpenCV here
Convert to HSV¶
def convert_to_hsv(image):
return cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
Convert To HSL¶
def convert_to_hsl(image):
return cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
Lets compare the results from HSV and HSL¶
hsv_images = list(map(convert_to_hsv, test_images))
hsl_images = list(map(convert_to_hsl, test_images))
HSV Images¶
show_images(hsv_images, image_name='hsv_test_images')
As you can see in the images above the white lines faded in the image especially the white dotted lines;However, it clearly highlights the lane lines that are in the shade in the bottom 4 images. So apparently HSV gives us the lane lines in the shade that we couldn't identify our RGB mask, but it fails to clearly identify other lines like in the first 2 images. Lets checkout the HSL image hopefully we'll have better luck with them.
HSL Images¶
show_images(hsl_images, image_name='hsl_test_images')
As we can see in the images above all lane lines are clear even the ones in the shade and the ones on bright surfaces. Now we need to build out white and yellow masks to filter the image for the only these colors.
Select Color¶
As we can see the HSL color space shows clearly defined lane lines so be it a Yellow or a while lane line.
You can also see the yellow colored lane lines as bluish hued lines. Now we need to select the lane lines as we did above, but here we'll do this by applying masks with changing the H(hue) S(saturation) L(lightness). We'll create a select_white_yellow_colors_HSL method just like our previous select_white_yellow_colors method to select our white and yellow lines, but this time we'll do it to the HSL image.
def select_white_yellow_colors_HSL(image):
# Convert the image to HSL
hsl_image = convert_to_hsl(image)
# White color mask
lower_white = np.uint8([0, 200, 0])
upper_white = np.uint8([255, 255, 255])
white_mask = cv2.inRange(hsl_image, lower_white, upper_white)
# Yellow color mask
lower_yellow = np.uint8([10, 0, 100])
upper_yellow = np.uint8([40, 255, 255])
yellow_mask = cv2.inRange(hsl_image, lower_yellow, upper_yellow)
# One mask
mask = cv2.bitwise_or(white_mask, yellow_mask)
masked_image = cv2.bitwise_and(image, image, mask = mask)
return masked_image
white_yellow_lines = list(map(select_white_yellow_colors_HSL, test_images))
show_images(white_yellow_lines, image_name='white_yellow_filtered_hsl_images')
Grayscaling and Smoothing the Image¶
In this section we grayscale our image to get it ready for Canny edge detection, we also need to smooth
our image to have sleek and better edges for the Canny edge to detect.
Gray scale the image¶
# grayscaling the image
# gray = grayscale(select_white_yellow_colors(test_images[0]))
gray = list(map(grayscale, white_yellow_lines))
show_images(gray, image_name='grayscaled_test_images')
Gaussian Blur smoothing¶
Here we smooth our grayscaled image using Gaussian Blur before we apply Canny edge detection, I found that a filter size of 11 does a nice job on most all the sample images. We can also use other odd values as our filter size, but I found that 11 is an ok enough value. Though Canny Edge Detection applies smoothing internally, we can't change the filter size so prior smoothing is recommended as a good practice.
# We apply Gaussian Blur to denoise the Gray image before getting canny edges
denoised_gray = [gaussian_blur(x, 11) for x in gray]
show_images(denoised_gray, image_name='smooth_test_images')
Canny Edge Detection¶
Canny Edge Detection is an algorithm that was developed by John F. Canny in 1986, where it takes a grayscale image and finds edges in the image where there is a shift or change in Gradient. We can set our boundaries for acceptable edges by setting the high and low thresholds represented as high_threshold and low_threshold respectively. Canny Edges Detection has multiple processes internally, and you can learn all about it here .
# Here we detect the image's edges using Canny edge detection
def canny_edges(image, low_threshold = 50, high_threshold = 150):
return canny(image, low_threshold, high_threshold)
canny_images = list(map(canny_edges, denoised_gray))
show_images(canny_images, image_name='canny_test_images')
Applying Mask (Region of interest)¶
In this section of the pipeline after selecting our colors, gray scaling, smoothing, and getting our image's Canny edges we remove all the unimportant parts of the image like other lane lines, trees, sky, etc. and we focus on the road ahead. We achieve this by choosing a region of interest, we'll be using the region_of_interest method provided in the helper methods. The method takes two arguments the image and am array or vertices that make up a polygon, it sets any pixel thats out side the polygon to zero (black) and leaves the pixels within the polygon intact. This will give us an image thats mostly black and white where the edges are within the defined polygon's borders.
def apply_region_of_interest(image):
# four sided polygon as a mask
imshape = image.shape
xsize = imshape[1]
ysize = imshape[0]
# Top-Left, Top-right, Bottom-right, Bottom-left
vertices = np.array([[(xsize * 0.05, ysize) # Bottom left
, (xsize * 0.45, ysize * 0.60) # Top left vertix 60% if the image's hight
, (xsize * 0.55, ysize * 0.60) # Top right vetrix
, (xsize * 0.95, ysize)]] # Bottom right
, dtype=np.int32)
return region_of_interest(image, vertices)
masked_edges_images = list(map(apply_region_of_interest, canny_images))
show_images(masked_edges_images, image_name='regionofinterest_test_images')
As you can see we get a good representation of the outlines of the lane lines, the region of interest's bottom is set to the image bottom and its top to 60% of the image hight (just below the image center) and I think that that is a good estimate for the horizon, at least for the given images. Also it spans the image with a little padding 5% and narrows towards the top of the image with a width of 10%, this gives a good representation of a road with its lane lines narrowing toward the horizon.
Note: I think that there is a better more dynamic way of detecting the road's horizon.
Hough Transform¶
Hough Transform is an technique (algorithm) that helps us identify lines in an image given a set of point on that image, in this case its Canny edges result. Hough transform uses something called Hough space which was create by Paul Hough in 1962, each point in Image space (white dots in our case) corresponds to a sine wave in Hough space, and if some points in image space lay on the same line in Image space this corresponds to an intersection (Hough point) of the points sine waves. One parameter of the hough_lines method is threshold which is the minimum number of intersections(votes) in Hough space to constitute a line in Image space. for more on Hough Transform visit: https://en.wikipedia.org/wiki/Hough_transform
Hough Space examples:¶
def hough_transform(image):
# Apply Hough transform on the Canny edged image
rho = 1 # distance resolution in pixels of the Hough grid
theta = 1 * (np.pi/180) # angular resolution in radians of the Hough grid
threshold = 20 # minimum number of votes (intersections in Hough grid cell)
min_line_len = 20 #minimum number of pixels making up a line
max_line_gap = 200 # maximum gap in pixels between connectable line segments
return hough_lines(image, rho, theta, threshold, min_line_len, max_line_gap)
images_hough_lines = list(map(hough_transform , masked_edges_images))
show_images([line_img for _, line_img in images_hough_lines], image_name='hough_lines_test_images')
Draw Lines on Image¶
I altered the return type of the hough_lines in the helpers methods to also return a list of lines along with the image with lines drawn on them, that will help in the next section where these lines will be divided into right and left lane lines, averaged, and weighted to produce the best fit for the lane lines.
images_copies = [np.copy(img) for img in test_images]
lines_images = []
for image, lines in zip(images_copies, images_hough_lines):
draw_lines(image, lines[0])
lines_images.append(image)
show_images(lines_images, image_name='hough_test_images')
As we can see in the images above the lane lines are clearly identified and a red line is drawn over the line segments connecting most dashed lines segments; however, we can still see the gaps in some of images.
Extrapolating Lane lines¶
After finding our lane lines and marking them we can still go a step further and try and extrapolate (extend) our lane lines to have two continues lane lines from the bottom of the image towards the horizon (60% of the image). We'll Achieve this by:
Averaging LaneLines¶
The average_slope_yintercept method below will return two lane lines (slope, y-intercept) by dividing our lane lines as left and right using their slopes negative slope means left lane and positive means right lane, then we'll average each lane line slopes and y-intercepts to get a single line for each lane. Also we'll use each line's length as a weight to give more consideration to lines that are longer in length.
def average_slope_yintercept(lines):
left_lane_lines = []
left_lane_weights = []
right_lane_lines = []
right_lane_weights = []
for line in lines:
for x1, y1, x2, y2 in line:
slope = (y2 - y1) / (x2 - x1)
y_intercept = y1 - slope * x1
# Right lane line
line_length = np.sqrt((y2-y1)**2 + (x2-x1)**2 )
if slope > 0:
right_lane_lines.append((slope, y_intercept))
right_lane_weights.append((line_length))
# Left lane line
else:
left_lane_lines.append((slope, y_intercept))
left_lane_weights.append((line_length))
# Weight slopes and Y_intercepts by their line lenght
right_lane = np.dot(right_lane_weights, right_lane_lines) / np.sum(right_lane_weights) if len(right_lane_weights) > 0 else None
left_lane = np.dot(left_lane_weights, left_lane_lines) / np.sum(left_lane_weights) if len(left_lane_weights) > 0 else None
return right_lane, left_lane
Making Lines to Draw¶
So we got the averaged slope and y-intercept for each lane line cool what now?
Well according to middle school math class y = mx + b is the equation on a straight line where:
mis the slope of the line which we got (yay)bis the y-intercept which we also have double (yay), we're almost done.
But wait what about the other two variables the points of the line?
Well x is unknown, but y will be the upped and lower bounds of our region of interest, so we only need to find x which we can do with a simple substitution x = (y - b) / m
def make_points(y1, y2, line):
if line is None:
return None
slope, intercept = line
x1 = int((y1 - intercept)/slope)
y1 = int(y1)
x2 = int((y2 - intercept)/slope)
y2 = int(y2)
return ((x1, y1), (x2, y2))
def make_lane_lines(image, lines):
right_lane, left_lane = average_slope_yintercept(lines)#seperate_lanelines(lines)
y1 = image.shape[0] # Y value Opencv has everything inverted
y2 = y1 * 0.60
right_line = make_points(y1, y2, right_lane)
left_line = make_points(y1, y2, left_lane)
return right_line, left_line
Drawing Lane Lines¶
I decided to make draw_lane_lines method to handle drawing the extrapolated lane lines instead of altering draw_lines method for better code organization.
def draw_lane_lines(image, lines, color=[255, 0, 0], thickness=2):
line_img = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)
for line in lines:
if line:
cv2.line(line_img, *line, color, thickness)
return weighted_img(image, line_img)
extrapolated_lane_lines_list = [make_lane_lines(img, hough_lines[0]) for img, hough_lines in zip(test_images, images_hough_lines)]
images_copies = [np.copy(img) for img in test_images]
lane_lines_images = []
for image, lane_lines in zip(images_copies, extrapolated_lane_lines_list):
lane_lines_images.append(draw_lane_lines(image, lane_lines, thickness=18))
show_images(lane_lines_images, image_name='extrapolated_test_images')
As we can see here it seems that all went according to plan and all the lines drawn extend the full length of the left and the right lane lines, all that remains now is to test it on the test videos.
Test on Videos¶
You know what's cooler than drawing lanes over images? Drawing lanes over video!
We can test our solution on two provided videos:
solidWhiteRight.mp4
solidYellowLeft.mp4
Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.
If you get an error that looks like this:
NeedDownloadError: Need ffmpeg exe.
You can download it by calling:
imageio.plugins.ffmpeg.download()Follow the instructions in the error message and check out this forum post for more troubleshooting tips across operating systems.
The Complete Pipeline¶
After applying the pipeline on the first video solidWhiteRight the result seemed ok though the lines were a little jittery.
However, after applying it to the second video solidYellowLeft the lane lines crossed each other for a second as you can see here:
Since as we know the video plays for 60 frames per second and each frame is an image, therefore we need to average the frames' lines to have a smoother and more consistent lane lines.
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from collections import deque
class LaneLineFinder:
SAMPLE_FRAMES = 30
def __init__(self):
self.right_lane_lines = deque(maxlen= self.SAMPLE_FRAMES)
self.left_lane_lines = deque(maxlen= self.SAMPLE_FRAMES)
def average_line_sampling(self, line, previous_lines):
if line:
previous_lines.append(line)
if len(previous_lines) > 0:
line = np.mean(previous_lines, axis = 0, dtype=np.int32) # Like tensorflow
line = tuple(map(tuple, line))
return line
def process_image(self, image):
# NOTE: The output you return should be a color image (3 channel) for processing video below
# TODO: put your pipeline here,
# you should return the final output (image where lines are drawn on lanes)
# Convert image to the HSL color space
white_yellow_image = select_white_yellow_colors_HSL(image)
# grayscaling the image
gray_image = grayscale(white_yellow_image)
# then we apply Gaussian Blur to denoice the Gray image before getting canny edges
# Denoise image using Gaussian Blur
denoised_image = gaussian_blur(gray_image, 11)
# then we apply canny edges to detect the image edges
canny_image = canny_edges(denoised_image)
# Apply region of intrest mask on the canny edges image
masked_image = apply_region_of_interest(canny_image)
# Apply Hough transform on the Canny edged image
# Applying Hough transform and drawing the lines on the image
image_lines = hough_transform(masked_image)
# Extrapolate lanelines return by the Hough transform
extrapolated_lane_lines = make_lane_lines(image, image_lines[0])
# Average the videos last 6 lines' frames
right_line = self.average_line_sampling(extrapolated_lane_lines[0], self.right_lane_lines)
left_line = self.average_line_sampling(extrapolated_lane_lines[1], self.left_lane_lines)
# Draw laneLines on the mage with lane lines colored
# image_copy = np.copy(image)
return draw_lane_lines(image, (right_line, left_line), thickness= 18)
Above I made a class called LaneLineFinder:
SAMPLE_FRAMESis a constant number of frames to average acrossright_lane_linesandleft_lane_linesare twodeque(queue in python) variables of lengthSAMPLE_FRAMESaverage_line_samplinggets the average of the previousSAMPLE_FRAMESframes using numpynp.meanmethodprocess_imagewhich executes the pipeline
A little test of the combined pipeline¶
lane_line_finder = LaneLineFinder()
x = lane_line_finder.process_image(test_images[0])
plt.imshow(x)
Let's try the one with the solid white lane on the right first ...
lane_line_finder = LaneLineFinder()
white_output = 'test_videos_output/solidWhiteRight.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4")
white_clip = clip1.fl_image(lane_line_finder.process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
#Close the reader properly
clip1.reader.close()
clip1.audio.reader.close_proc()
Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice.
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
Now for the one with the solid yellow lane on the left. This one's more tricky!
lane_line_finder = LaneLineFinder()
yellow_output = 'test_videos_output/solidYellowLeft.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)
clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(lane_line_finder.process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)
#Close the reader properly
clip2.reader.close()
clip2.audio.reader.close_proc()
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(yellow_output))
Optional Challenge¶
Try your lane finding pipeline on the video below. Does it still work? Can you figure out a way to make it more robust? If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!
lane_line_finder = LaneLineFinder()
challenge_output = 'test_videos_output/challenge.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)
clip3 = VideoFileClip('test_videos/challenge.mp4')
challenge_clip = clip3.fl_image(lane_line_finder.process_image)
%time challenge_clip.write_videofile(challenge_output, audio=False)
#Close the reader properly
clip3.reader.close()
clip3.audio.reader.close_proc()
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(challenge_output))